3D generative modeling has shown much improvement recently with models such as GET3D [1]} delivering impressive high quality results with rich geometric details. GET3D is trained to generate 3D textured meshes for a category and produces impressive 3D objects for categories like Car, Chair, and Motorcycle using data from ShapeNet [2]}. Objaverse contains 3D models for many diverse categories including tail categories which are not represented in other datasets. It also contains diverse and realistic object instances per category. This scale and diversity can be used to train large vocabulary and high quality 3D generative models. In this work, we showcase the potential of this data as follows. We choose three categories of objects, Shoe, Bag, and Fruit&Veg, and subsample objects from Objaverse to create three separate datasets containing, respectively, 143 shoes, 816 bags, and 571 fruits & vegetables (116 apples, 112 gourds, 92 mushrooms, 68 bananas, 52 oranges, 52 pears, 31 potatoes 24 lemons, and 24 pineapples). For comparison, we also train a GET3D model on the set of 83 bags from the ShapeNet dataset. Fig. REF  shows a collection of 3D objects generated by our trained GET3D models. Qualitatively, the 3D-meshes generated by the Objaverse-trained models are high-quality and diverse, especially when compared to the generations from the ShapeNet-trained model. To quantify this observation, we asked crowdworkers to rate the diversity of Bag generations produced by the Objaverse and ShapeNet trained models. When shown collections of nine randomly sampled generations from both models, workers rated the collection generated from the Objaverse trained model as more diverse in appearance 91% of the time.
